1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A data scientist is preparing to fine-tune a foundation model on OCI. Which two actions should they take to optimize costs? (Select TWO.)
⚠ Common exam trap
Oracle often tests the misconception that spot/preemptible instances are universally cost-effective for all AI workloads, but in OCI, they are not supported for interactive or stateful fine-tuning jobs, making Option C a classic distractor.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use the smallest model that meets accuracy requirements
Using the smallest model that meets accuracy requirements directly reduces the number of parameters and computational operations required during fine-tuning. On OCI, larger models consume significantly more GPU memory and compute hours, so selecting the minimal viable model minimizes both training time and associated costs. This aligns with cost optimization best practices for generative AI workloads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use the smallest model that meets accuracy requirements
Why this is correct
Correct: Smaller models require less compute and memory.
- ✗
Use a single OCPU shape to minimize per-hour cost
Why it's wrong here
Incorrect: Fine-tuning typically requires GPU shapes; single OCPU is insufficient.
- ✗
Use spot preemptible instances to save on compute
Why it's wrong here
Incorrect: Preemptible instances may be terminated during long fine-tuning jobs.
- ✓
Monitor fine-tuning progress and stop early if validation loss plateaus
Why this is correct
Correct: Early stopping saves compute costs.
- ✗
Store training data in Archive Storage to reduce storage costs
Why it's wrong here
Incorrect: Archive Storage has high retrieval latency and costs for frequent access.
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